This notebook contains a set of analyses for analyzing ZeeGarcia’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
ZeeGarcia | training | published before 2020 | 1,632 | 2,057 |
ZeeGarcia | validation | published 2020 | 58 | 85 |
ZeeGarcia | test | published after 2020 | 62 | 65 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
ZeeGarcia | ZMan Games | 6.1% | 1.0% | 5.86 |
ZeeGarcia | Rio Grande Games | 7.0% | 1.5% | 4.75 |
ZeeGarcia | Asmodee | 9.3% | 2.1% | 4.44 |
ZeeGarcia | Fantasy Flight Games | 4.0% | 0.9% | 4.32 |
ZeeGarcia | Tricktaking | 5.0% | 1.5% | 3.40 |
ZeeGarcia | Games With Solitaire Rules | 9.4% | 4.9% | 1.94 |
ZeeGarcia | Card Game | 46.1% | 28.1% | 1.64 |
ZeeGarcia | Parker Brothers | 2.3% | 2.5% | 0.92 |
ZeeGarcia | Miniatures Game | 2.4% | 5.0% | 0.47 |
ZeeGarcia | Movies TV Radio Theme | 2.3% | 5.2% | 0.45 |
ZeeGarcia | Self-Published | 1.0% | 2.8% | 0.35 |
ZeeGarcia | Action Dexterity | 1.5% | 5.5% | 0.28 |
ZeeGarcia | Childrens Game | 1.7% | 8.5% | 0.20 |
ZeeGarcia | Wargame | 1.5% | 20.2% | 0.07 |
ZeeGarcia | Simulation | 0.8% | 11.0% | 0.07 |
ZeeGarcia | Movement Points | 0.2% | 2.6% | 0.07 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2016 | 200147 | Kanagawa | 0.994 | yes |
2 | 2015 | 176920 | Mission: Red Planet (Second Edition) | 0.993 | yes |
3 | 2006 | 22141 | Cleopatra and the Society of Architects | 0.993 | yes |
4 | 2015 | 177639 | Raptor | 0.993 | yes |
5 | 2009 | 54998 | Cyclades | 0.991 | yes |
6 | 2005 | 15062 | Shadows over Camelot | 0.991 | yes |
7 | 2003 | 6068 | Queen's Necklace | 0.991 | yes |
8 | 2000 | 478 | Citadels | 0.988 | yes |
9 | 2015 | 173346 | 7 Wonders Duel | 0.988 | yes |
10 | 2005 | 18258 | Mission: Red Planet | 0.987 | yes |
11 | 2007 | 29030 | Chicago Poker | 0.986 | yes |
12 | 2008 | 33107 | Senji | 0.985 | yes |
13 | 2016 | 205398 | Citadels | 0.984 | yes |
14 | 2014 | 155987 | Abyss | 0.981 | yes |
15 | 2018 | 239840 | Micropolis | 0.976 | yes |
16 | 2014 | 157354 | Five Tribes | 0.976 | yes |
17 | 2013 | 134453 | The Little Prince: Make Me a Planet | 0.969 | yes |
18 | 2019 | 251830 | Alhambra: Mega Box | 0.968 | yes |
19 | 2011 | 70919 | Takenoko | 0.967 | yes |
20 | 2019 | 265285 | Queenz: To Bee or Not to Bee | 0.964 | yes |
21 | 2017 | 213893 | Yamataï | 0.963 | yes |
22 | 2004 | 10997 | Boomtown | 0.963 | yes |
23 | 2012 | 116858 | Noah | 0.963 | yes |
24 | 2016 | 205610 | A Game of Thrones: Hand of the King | 0.960 | yes |
25 | 2019 | 281960 | Kingdomino Duel | 0.957 | yes |
26 | 2009 | 40793 | Dice Town | 0.954 | yes |
27 | 2012 | 129904 | Shadows over Camelot: The Card Game | 0.947 | no |
28 | 2011 | 103686 | Mundus Novus | 0.944 | yes |
29 | 2019 | 276042 | Conspiracy: Abyss Universe | 0.944 | yes |
30 | 2017 | 197178 | DIG | 0.939 | yes |
31 | 2016 | 201920 | Pocket Madness | 0.938 | yes |
32 | 2018 | 259829 | Loser | 0.936 | no |
33 | 2018 | 199792 | Everdell | 0.935 | yes |
34 | 2014 | 165662 | Haru Ichiban | 0.934 | yes |
35 | 2012 | 125311 | Okiya | 0.925 | yes |
36 | 2016 | 204583 | Kingdomino | 0.923 | yes |
37 | 2006 | 24845 | Tomahawk | 0.920 | yes |
38 | 2017 | 221107 | Pandemic Legacy: Season 2 | 0.918 | no |
39 | 1997 | 42 | Tigris & Euphrates | 0.917 | yes |
40 | 2014 | 150926 | Roll Through the Ages: The Iron Age | 0.914 | yes |
41 | 2000 | 823 | The Lord of the Rings | 0.912 | yes |
42 | 2001 | 878 | Wyatt Earp | 0.912 | yes |
43 | 2010 | 67185 | Sobek | 0.911 | yes |
44 | 2019 | 270971 | Era: Medieval Age | 0.908 | no |
45 | 2004 | 12942 | No Thanks! | 0.908 | yes |
46 | 2019 | 286096 | Tapestry | 0.905 | no |
47 | 2014 | 154443 | Madame Ching | 0.904 | yes |
48 | 2016 | 193210 | Dice Stars | 0.903 | yes |
49 | 2013 | 143157 | SOS Titanic | 0.899 | yes |
50 | 2009 | 40237 | Long Shot | 0.896 | yes |
51 | 2019 | 285984 | Last Bastion | 0.894 | yes |
52 | 2005 | 18588 | Les Fils de Samarande | 0.892 | no |
53 | 2016 | 190639 | Zany Penguins | 0.891 | yes |
54 | 2019 | 244191 | Naga Raja | 0.890 | yes |
55 | 2019 | 265736 | Tiny Towns | 0.889 | no |
56 | 2016 | 182120 | Histrio | 0.888 | yes |
57 | 2010 | 73439 | Troyes | 0.884 | no |
58 | 2004 | 9216 | Goa | 0.881 | yes |
59 | 2010 | 68448 | 7 Wonders | 0.879 | yes |
60 | 2017 | 192827 | RUM | 0.879 | yes |
61 | 2014 | 148228 | Splendor | 0.878 | yes |
62 | 2016 | 205637 | Arkham Horror: The Card Game | 0.873 | yes |
63 | 2012 | 129622 | Love Letter | 0.873 | yes |
64 | 2004 | 9509 | Iglu Iglu | 0.872 | yes |
65 | 2010 | 72287 | Mr. Jack Pocket | 0.872 | yes |
66 | 2004 | 14781 | Drôles de Zèbres | 0.868 | yes |
67 | 2002 | 4471 | Fist of Dragonstones | 0.867 | yes |
68 | 2015 | 158915 | GEM | 0.866 | yes |
69 | 2009 | 55427 | Mr. Jack in New York | 0.864 | yes |
70 | 2013 | 148290 | Longhorn | 0.863 | yes |
71 | 2004 | 9220 | Saboteur | 0.863 | yes |
72 | 2006 | 28025 | Wicked Witches Way | 0.861 | yes |
73 | 2008 | 34635 | Stone Age | 0.861 | no |
74 | 2011 | 91523 | Mondo | 0.861 | no |
75 | 2005 | 20090 | Double Agent | 0.861 | yes |
76 | 2018 | 247236 | Duelosaur Island | 0.860 | no |
77 | 2003 | 8129 | Sluff Off! | 0.857 | yes |
78 | 2011 | 108783 | Dr. Shark | 0.853 | no |
79 | 2017 | 232043 | Queendomino | 0.853 | yes |
80 | 2013 | 145645 | Le Fantôme de l'Opéra | 0.851 | yes |
81 | 2006 | 21763 | Mr. Jack | 0.849 | yes |
82 | 1995 | 915 | Mystery of the Abbey | 0.849 | yes |
83 | 2017 | 192829 | SOW | 0.847 | yes |
84 | 2007 | 28738 | Kamon | 0.846 | yes |
85 | 2009 | 29096 | Letter of Marque | 0.845 | no |
86 | 2008 | 37380 | Roll Through the Ages: The Bronze Age | 0.842 | yes |
87 | 2016 | 160010 | Conan | 0.839 | yes |
88 | 2010 | 65907 | Mystery Express | 0.838 | no |
89 | 2017 | 200847 | Secrets | 0.835 | no |
90 | 2011 | 100423 | Elder Sign | 0.834 | yes |
91 | 2019 | 265031 | Ice Team | 0.833 | no |
92 | 2018 | 257499 | Arkham Horror (Third Edition) | 0.833 | yes |
93 | 2016 | 192153 | Pandemic: Reign of Cthulhu | 0.833 | yes |
94 | 2013 | 143693 | Glass Road | 0.831 | no |
95 | 2018 | 244330 | Scarabya | 0.830 | yes |
96 | 2014 | 154600 | Desperados of Dice Town | 0.830 | yes |
97 | 2014 | 132531 | Roll for the Galaxy | 0.827 | yes |
98 | 2019 | 283864 | Trails of Tucana | 0.827 | yes |
99 | 2018 | 241478 | Kiwara | 0.823 | yes |
100 | 2015 | 161383 | LIE | 0.820 | yes |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
GLM | roc_auc | binary | 0.84 |
Decision Tree | roc_auc | binary | 0.76 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think ZeeGarcia is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2012 | 129904 | Shadows over Camelot: The Card Game | 0.947 | no |
2018 | 259829 | Loser | 0.936 | no |
2017 | 221107 | Pandemic Legacy: Season 2 | 0.918 | no |
2019 | 270971 | Era: Medieval Age | 0.908 | no |
2019 | 286096 | Tapestry | 0.905 | no |
What games does the model think ZeeGarcia is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2015 | 174155 | Lignum | 0.001 | yes |
2006 | 25758 | Trüffel-Schnüffel | 0.003 | yes |
2002 | 11873 | AMC Reel Clues | 0.003 | yes |
2014 | 165401 | Wir sind das Volk! | 0.004 | yes |
2007 | 28843 | 300: The Board Game | 0.004 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Noah | The Little Prince: Make Me a Planet | Abyss | Mission: Red Planet (Second Edition) | Kanagawa | Yamataï | Micropolis | Alhambra: Mega Box |
2 | Shadows over Camelot: The Card Game | SOS Titanic | Five Tribes | Raptor | Citadels | DIG | Loser | Queenz: To Bee or Not to Bee |
3 | Okiya | Longhorn | Haru Ichiban | 7 Wonders Duel | A Game of Thrones: Hand of the King | Pandemic Legacy: Season 2 | Everdell | Kingdomino Duel |
4 | Love Letter | Le Fantôme de l'Opéra | Roll Through the Ages: The Iron Age | GEM | Pocket Madness | RUM | Duelosaur Island | Conspiracy: Abyss Universe |
5 | Android: Netrunner | Glass Road | Madame Ching | LIE | Kingdomino | Queendomino | Arkham Horror (Third Edition) | Era: Medieval Age |
6 | Button Up! | Sheepzzz | Splendor | 504 | Dice Stars | SOW | Scarabya | Tapestry |
7 | Sky Tango | Cappuccino | Desperados of Dice Town | Mysterium | Zany Penguins | Secrets | Kiwara | Last Bastion |
8 | Zombicide | Pentos | Roll for the Galaxy | Sylvion | Histrio | Nut | Greedy Kingdoms | Naga Raja |
9 | Robinson Crusoe: Adventures on the Cursed Island | Room 25 | Onirim (Second Edition) | Pandemic Legacy: Season 1 | Arkham Horror: The Card Game | BOO | Jurassic Snack | Tiny Towns |
10 | Descent: Journeys in the Dark (Second Edition) | Mascarade | Imperial Settlers | Tong | Conan | King's Life | Imaginarium | Ice Team |
11 | Zooloretto: The Dice Game | Cinque Terre | Patchwork | Arboretum | Pandemic: Reign of Cthulhu | Jump Drive | Pandemic: Fall of Rome | Trails of Tucana |
12 | Targi | Gravwell: Escape from the 9th Dimension | Akrotiri | Viticulture Essential Edition | Crazy Mistigri | BOX | KeyForge: Call of the Archons | The Magnificent |
13 | Agricola: All Creatures Big and Small | Asante | Pandemic: The Cure | Plums | HMS Dolores | Legend of the Five Rings: The Card Game | Railroad Ink: Deep Blue Edition | Ishtar: Gardens of Babylon |
14 | Africana | Legacy: The Testament of Duke de Crecy | Nations: The Dice Game | SHH | Star Wars: Destiny | WOO | Treasure Island | Wingspan |
15 | Il Vecchio | Bruges | Blue Moon Legends | Elysium | Honshū | Azul | Hokkaido | KeyForge: Age of Ascension |
16 | Divinare | Euphoria: Build a Better Dystopia | Roll Through the Ages: The Iron Age with Mediterranean Expansion | Mondo: Der rasante Legespaß | Bloodborne: The Card Game | Oliver Twist | Fist of Dragonstones: The Tavern Edition | Sierra West |
17 | Ginkgopolis | BANG! The Dice Game | Fields of Arle | BUS | Mansions of Madness: Second Edition | Majesty: For the Realm | Rebel Nox | Run Fight or Die: Reloaded |
18 | Tokaido | BodgerMania | Dragon Run | The Little Prince: Rising to the Stars | Covert | GYM | Underwater Cities | Marvel Champions: The Card Game |
19 | Gentlemen Thieves | Terror in Meeple City | Age of War | Bastion | Black Orchestra | Miaui | New Frontiers | Herbaceous Sprouts |
20 | Think Again! | Forbidden Desert | Saboteur: The Duel | HUE | Explorers of the North Sea | Ex Libris | Architects of the West Kingdom | Three-Dragon Ante: Legendary Edition |
21 | Thunderstone Advance: Towers of Ruin | The Ravens of Thri Sahashri | Continental Express | Celestia | Archaeology: The New Expedition | Spirit Island | Dragons | Cthulhu: Death May Die |
22 | Escape: The Curse of the Temple | Patronize | Chimera | OctoDice | The Castles of Burgundy: The Card Game | Pandemic: Rising Tide | Holmes and Moriarty | Victorian Masterminds |
23 | Wiz-War (Eighth Edition) | Thunderstone: Starter Set | Warhammer 40,000: Conquest | Valley of the Kings: Afterlife | Love Letter Premium | The Fox in the Forest | Book of Dragons | Coralia |
24 | The Hobbit Card Game | Scotland Yard Master | Dragon's Hoard | SteamRollers | Smash Up: Cease and Desist | Atlas: Enchanted Lands | Railroad Ink: Blazing Red Edition | Amul |
25 | Antartik | Five Cucumbers | DungeonQuest Revised Edition | Cthulhu's Vault | Inis | ORC | Lords of Hellas | Silver & Gold |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
ZeeGarcia | owned | validation | GLM | roc_auc | 0.755 |
ZeeGarcia | owned | validation | Decision Tree | roc_auc | 0.684 |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.950 | yes |
2020 | 297661 | Gold River | 0.937 | yes |
2020 | 323262 | Velonimo | 0.936 | yes |
2020 | 229782 | Roland Wright: The Dice Game | 0.924 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.885 | yes |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.867 | yes |
2020 | 292917 | Mosquito Show | 0.832 | no |
2020 | 297666 | Jurassic Brunch | 0.795 | yes |
2020 | 303672 | Trek 12: Himalaya | 0.753 | yes |
2020 | 288169 | The Fox in the Forest Duet | 0.727 | no |
2020 | 283155 | Calico | 0.677 | no |
2020 | 301880 | Raiders of Scythia | 0.674 | no |
2020 | 291457 | Gloomhaven: Jaws of the Lion | 0.626 | yes |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.615 | yes |
2020 | 300010 | Dragomino | 0.605 | no |
2020 | 298572 | Cosmic Encounter Duel | 0.604 | no |
2020 | 293678 | Stellar | 0.599 | no |
2020 | 293556 | Gloomy Graves | 0.584 | no |
2020 | 318983 | Faiyum | 0.554 | no |
2020 | 262208 | Dungeon Drop | 0.504 | no |
2020 | 270109 | Iwari | 0.501 | yes |
2020 | 267009 | Rome & Roll | 0.494 | no |
2020 | 296151 | Viscounts of the West Kingdom | 0.494 | no |
2020 | 233262 | Tidal Blades: Heroes of the Reef | 0.493 | no |
2020 | 245659 | Vampire: The Masquerade – Vendetta | 0.488 | no |
2020 | 306481 | Tawantinsuyu: The Inca Empire | 0.484 | no |
2020 | 295486 | My City | 0.480 | no |
2020 | 298371 | Wild Space | 0.478 | no |
2020 | 312804 | Pendulum | 0.456 | no |
2020 | 325635 | Unmatched: Little Red Riding Hood vs. Beowulf | 0.448 | no |
2020 | 294484 | Unmatched: Cobble & Fog | 0.444 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.440 | no |
2020 | 307844 | Atheneum: Mystic Library | 0.433 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.429 | yes |
2020 | 303054 | Yacht Rock | 0.423 | no |
2020 | 313698 | Monster Expedition | 0.422 | no |
2020 | 301399 | Lyttle Wood | 0.418 | yes |
2020 | 296512 | The Game: Quick & Easy | 0.413 | yes |
2020 | 302310 | Nanaki | 0.408 | no |
2020 | 300531 | Paleo | 0.403 | yes |
2020 | 293309 | Kraken Attack! | 0.401 | no |
2020 | 296667 | Vintage | 0.400 | no |
2020 | 184267 | On Mars | 0.397 | no |
2020 | 293014 | Nidavellir | 0.396 | no |
2020 | 294232 | Stolen Paintings | 0.387 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.384 | no |
2020 | 299592 | Beez | 0.383 | no |
2020 | 299607 | Capital Lux 2: Generations | 0.380 | no |
2020 | 324345 | キャットインザボックス (Cat in the box) | 0.379 | no |
2020 | 284777 | Unmatched: Jurassic Park – InGen vs Raptors | 0.378 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 2021 | 332944 | Sobek: 2 Players | 0.958 | yes |
2 | 2021 | 340041 | Kingdomino Origins | 0.848 | yes |
3 | 2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.810 | no |
4 | 2021 | 329670 | Pandemic: Hot Zone – Europe | 0.777 | no |
5 | 2021 | 344415 | Trek 12: Amazonia | 0.691 | no |
6 | 2021 | 334644 | Nicodemus | 0.686 | yes |
7 | 2021 | 329714 | Dreadful Circus | 0.686 | no |
8 | 2021 | 303676 | Oh My Brain | 0.681 | yes |
9 | 2021 | 290236 | Canvas | 0.665 | no |
10 | 2022 | 295374 | Long Shot: The Dice Game | 0.659 | yes |
11 | 2021 | 314491 | Meadow | 0.627 | no |
12 | 2021 | 340466 | Unfathomable | 0.617 | no |
13 | 2021 | 328535 | Mandragora | 0.609 | yes |
14 | 2021 | 331635 | Kameloot | 0.603 | no |
15 | 2021 | 340237 | Wonder Book | 0.572 | no |
16 | 2021 | 285967 | Ankh: Gods of Egypt | 0.560 | yes |
17 | 2021 | 339906 | The Hunger | 0.552 | no |
18 | 2021 | 304783 | Hadrian's Wall | 0.551 | no |
19 | 2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.546 | no |
20 | 2021 | 329465 | Red Rising | 0.545 | yes |
21 | 2022 | 353470 | Star Wars: Jabba's Palace – A Love Letter Game | 0.542 | no |
22 | 2021 | 340834 | Gravwell: 2nd Edition | 0.536 | no |
23 | 2023 | 349793 | Age of Rome | 0.528 | no |
24 | 2021 | 340677 | Bad Company | 0.527 | no |
25 | 2021 | 315937 | X-Men: Mutant Insurrection | 0.526 | no |
26 | 2021 | 339789 | Welcome to the Moon | 0.515 | no |
27 | 2022 | 315610 | Massive Darkness 2: Hellscape | 0.514 | no |
28 | 2022 | 332393 | Bridge City Poker | 0.505 | no |
29 | 2021 | 341358 | INSERT | 0.494 | no |
30 | 2022 | 341945 | La Granja: Deluxe Master Set | 0.494 | no |
31 | 2022 | 338364 | Pumafiosi | 0.492 | no |
32 | 2022 | 356033 | Libertalia: Winds of Galecrest | 0.477 | no |
33 | 2021 | 324242 | Sheepy Time | 0.468 | no |
34 | 2021 | 356907 | Mascarade (second edition) | 0.458 | no |
35 | 2021 | 316080 | KeyForge: Dark Tidings | 0.441 | no |
36 | 2021 | 339905 | Love Letter: Princess Princess Ever After | 0.429 | no |
37 | 2021 | 344408 | Full Throttle! | 0.425 | no |
38 | 2021 | 329084 | Space Dragons | 0.422 | no |
39 | 2021 | 333553 | For the King (and Me) | 0.421 | no |
40 | 2022 | 308028 | Drop Drive | 0.420 | no |
41 | 2021 | 329529 | Magellan: Elcano | 0.419 | no |
42 | 2021 | 344258 | That Time You Killed Me | 0.390 | no |
43 | 2022 | 356996 | The Border | 0.389 | no |
44 | 2022 | 275215 | Namiji | 0.378 | no |
45 | 2021 | 322014 | All-Star Draft | 0.373 | no |
46 | 2021 | 310198 | Escape: Roll & Write | 0.368 | no |
47 | 2021 | 346703 | 7 Wonders: Architects | 0.368 | no |
48 | 2021 | 336382 | Marvel United: X-Men | 0.366 | no |
49 | 2021 | 295947 | Cascadia | 0.366 | yes |
50 | 2021 | 316343 | Capital Lux 2: Pocket | 0.365 | no |
51 | 2021 | 324856 | The Crew: Mission Deep Sea | 0.362 | no |
52 | 2021 | 320069 | Tavern Tales: Legends of Dungeon Drop | 0.359 | no |
53 | 2021 | 313262 | Shamans | 0.358 | no |
54 | 2021 | 335541 | We Care: a Grizzled Game | 0.350 | no |
55 | 2021 | 322339 | Hanamikoji: Geisha's Road | 0.350 | no |
56 | 2021 | 286751 | Zombicide: 2nd Edition | 0.343 | no |
57 | 2022 | 338460 | The Isle of Cats: Explore & Draw | 0.338 | no |
58 | 2021 | 333055 | Subastral | 0.337 | no |
59 | 2021 | 331549 | MiniQuest Adventures | 0.333 | no |
60 | 2022 | 335764 | Unmatched: Battle of Legends, Volume Two | 0.329 | no |
61 | 2022 | 324894 | Free Radicals | 0.329 | no |
62 | 2021 | 339790 | Cocktail | 0.328 | no |
63 | 2021 | 305682 | Rolling Realms | 0.319 | no |
64 | 2021 | 322282 | Momiji | 0.314 | no |
65 | 2021 | 286667 | Tutankhamun | 0.313 | no |
66 | 2021 | 346553 | Heuschrecken Poker | 0.313 | no |
67 | 2021 | 335678 | Let's Make a Bus Route: The Dice Game | 0.302 | yes |
68 | 2022 | 350316 | Wayfarers of the South Tigris | 0.301 | no |
69 | 2021 | 281248 | Cape May | 0.300 | no |
70 | 2022 | 353765 | Awimbawé | 0.296 | no |
71 | 2021 | 344405 | Cartaventura: Oklahoma | 0.296 | no |
72 | 2021 | 330608 | Cryo | 0.296 | no |
73 | 2021 | 342073 | Berried Treasure | 0.294 | no |
74 | 2021 | 323156 | Stroganov | 0.293 | no |
75 | 2021 | 275557 | The Last Bottle of Rum | 0.291 | no |
76 | 2021 | 337765 | Brian Boru: High King of Ireland | 0.285 | no |
77 | 2022 | 319910 | Pagan: Fate of Roanoke | 0.283 | no |
78 | 2022 | 275284 | Arkeis | 0.281 | no |
79 | 2021 | 300523 | Biblios: Quill and Parchment | 0.280 | no |
80 | 2021 | 341048 | Free Ride | 0.279 | no |
81 | 2021 | 283242 | The Whatnot Cabinet | 0.277 | no |
82 | 2021 | 300305 | Nanga Parbat | 0.275 | no |
83 | 2021 | 311990 | Macaron | 0.274 | no |
84 | 2021 | 316287 | Quest | 0.273 | no |
85 | 2021 | 344839 | Dog Lover | 0.272 | yes |
86 | 2021 | 345036 | Qwixx Longo | 0.271 | no |
87 | 2021 | 292899 | Tribune | 0.268 | no |
88 | 2021 | 331946 | Faux Diamonds | 0.266 | no |
89 | 2021 | 259962 | Stress Botics | 0.263 | no |
90 | 2022 | 283137 | Human Punishment: The Beginning | 0.261 | no |
91 | 2021 | 304324 | Dive | 0.261 | no |
92 | 2021 | 295607 | Canopy | 0.259 | yes |
93 | 2021 | 322560 | Maeshowe: an Orkney Saga | 0.259 | no |
94 | 2021 | 282776 | Tumble Town | 0.257 | no |
95 | 2022 | 281258 | Sub Terra II: Inferno's Edge | 0.255 | no |
96 | 2021 | 338628 | TRAILS | 0.255 | yes |
97 | 2021 | 257706 | Zoo-ography | 0.254 | no |
98 | 2022 | 310873 | Carnegie | 0.252 | no |
99 | 2021 | 341362 | Reapers | 0.252 | no |
100 | 2022 | 319807 | Shogun no Katana | 0.251 | no |
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 6043287 322.8 13980205 746.7 NA 13980205 746.7
## Vcells 172655085 1317.3 558901608 4264.1 102400 1113448843 8495.0